Device, method and computer program for detecting characters in an image

ABSTRACT

A device for detecting characters in an image includes a Hough transformer implemented to identify, as identified elements of writing, circular arcs or elliptical arcs in the image or in a preprocessed version of the image. The device further includes a character description generator implemented to obtain, on the basis of the identified circular arcs or elliptical arcs, a character description which describes locations of the identified circular arcs or elliptical arcs. In addition, the device includes a database comparator implemented to compare the character description with a plurality of comparative character descriptions which have character codes associated with them, so as to provide, as a result of the comparison, a character code of a detected character.

The present invention generally relates to a device, a method and acomputer program for identifying a traffic sign in an image,specifically to character detection while using a Hough transform.

BACKGROUND OF THE INVENTION

A multitude of algorithms have already been known whose goal is toidentify characters in an image, for example on a scanned page. In themethod mentioned, templates describing known characters are directlycompared to the image, for example. However, such a comparison of atemplate with an image is extremely costly in terms of computingexpenditure. In addition, most known methods have a low level ofreliability if different fonts are to be detected. Also, the detectionof handwriting is particularly problematic since, as is known, there arehuge differences between the different persons' handwriting.

SUMMARY

According to an embodiment, a device for detecting characters) in animage may have: a Hough transformer implemented to identify, asidentified elements of writing, circular arcs or elliptical arcs in theimage or within a preprocessed version of the image; a characterdescription generator implemented to acquire, on the basis of theidentified circular arcs or elliptical arcs, a character descriptionwhich describes locations of the identified circular arcs or ellipticalarcs; and a database comparator implemented to compare the characterdescription with a plurality of comparative character descriptions whichhave character codes associated with them, so as to provide, as a resultof the comparison, a character code of a detected character.

According to another embodiment, a method of detecting characters in animage may have the steps of: Hough transforming the image or apreprocessed version of the image so as to identify, as identifiedelements of writing, circular arcs or elliptical arcs in the image or ina preprocessed version of the image; producing a character descriptionon the basis of the identified circular arcs or elliptical arcs, thecharacter description describing locations of the identified circulararcs or elliptical arcs; and comparing the character description with aplurality of comparative character descriptions having character codesassociated with them, so as to provide, as a result of the comparison, acharacter code of a detected character.

According to another embodiment, a method of detecting characters in animage may have the steps of: Hough transforming the image or apreprocessed version of the image to identify, as identified elements ofwriting, in the image or in the preprocessed image, a plurality ofstraight line sections which run through the image in differentdirections; producing a character description on the basis of theidentified straight line sections, the character description describinglocations of the identified straight line sections; and comparing thecharacter description with a plurality of comparative characterdescriptions having character codes associated with them, so as toprovide, as a result of the comparison, a character code of a detectedcharacter.

Another embodiment may have a computer program for performing the methodof detecting characters in an image, wherein the method may have thesteps of: Hough transforming the image or a preprocessed version of theimage so as to identify, as identified elements of writing, circulararcs or elliptical arcs in the image or in a preprocessed version of theimage; producing a character description on the basis of the identifiedcircular arcs or elliptical arcs, the character description describinglocations of the identified circular arcs or elliptical arcs; andcomparing the character description with a plurality of comparativecharacter descriptions having character codes associated with them, soas to provide, as a result of the comparison, a character code of adetected character, when the computer program runs on a computer.

Another embodiment may have a computer program for performing the methodof detecting characters in an image, wherein the method may have thesteps of: Hough transforming the image or a preprocessed version of theimage to identify, as identified elements of writing, in the image or inthe preprocessed image, a plurality of straight line sections which runthrough the image in different directions; producing a characterdescription on the basis of the identified straight line sections, thecharacter description describing locations of the identified straightline sections; and comparing the character description with a pluralityof comparative character descriptions having character codes associatedwith them, so as to provide, as a result of the comparison, a charactercode of a detected character, when the computer program runs on acomputer.

The present invention provides a device for detecting characters in theimage. The device comprises a Hough transformer implemented to identifycircular arcs (or circular arc segments) or elliptical arcs (orelliptical arc segments) in the image or in a preprocessed version ofthe image as identified elements of writing. The device furthercomprises a character description generator implemented to obtain, onthe basis of the identified circular arcs or elliptical arcs, acharacter description which describes a location of the identifiedcircular arcs or elliptical arcs. In addition, the device comprises adatabase comparator implemented to compare the character descriptionwith a plurality of comparative character descriptions which havecharacter codes associated with them, so as to provide, as the result ofthe comparison, a character code of a detected character.

Alternatively, the Hough transformer is implemented to identify aplurality of straight line sections, running through the image indifferent directions, as identified elements of writing. In this case,the character description generator is implemented to obtain, on thebasis of the identified straight line sections, a character descriptionwhich describes locations of the identified straight line sections.

It is the core idea of the present invention that elements of writing,i.e. circular arcs, elliptical arcs or straight line sections, may beidentified by a Hough transformer in a particularly advantageous manner,and that the locations of the elements of writing thus identifiedrepresent a characteristic character description which may efficientlybe used for identifying the characters.

In other words, it has been found that identification of individualelements of writing, i.e. of arcs or straight line sections, enablesefficient preprocessing. A character is broken down, by theidentification of elements of writing performed within the Houghtransformer, into a plurality of clearly defined individual elements,namely into a plurality of individual circular arcs, elliptical arcs,and/or straight line sections. This offers the possibility of describingcharacters by a small number of parameters, namely, for example, bymeans of the locations of the identified elements of writing. identifiedThe elements of writing, or their location parameters, thereforerepresent forms of description which are suited for a particularlyefficient database comparison.

For example, if a character consists of many thousands of image points,or pixels (e.g. 100 image points times 100 image points=10,000 imagepoints), the character description produced in the inventive manner willonly provide, e.g., a total of four location parameters of four arcs,for example when the character only comprises four arcs (e.g. “o”, “s”).

The location parameters of the identified elements of writing thereforeare extremely well suited for efficient database comparison, and furtherrepresent characteristic information about a character. Specifically,various characters differ specifically in terms of the locations of theindividual elements of writing (arcs and straight line sections).

In this respect it shall be noted that, e.g., a person, when he/she iswriting down a character, composes the character of a plurality of (forexample continuously or separately) successive elements of writing.However, it is precisely the shapes and locations of the elements ofwriting that are decisive for which character has been reproduced.

In addition it shall be noted that, by using a Hough transform,detection of characters in scanned originals of low quality may beimproved considerably as compared to conventional methods. For example,a Hough transformer is able to detect even such line-shaped curves as acoherent curve which comprise comparatively short interruptions.However, specifically in the reproduction of handwriting, it is not rarefor characters to be interrupted. For example, if a sheet of paper fullof writing is scanned at a low resolution, it may occur that individuallines (particularly thin lines) are not fully reproduced. A Houghtransformer is capable of detecting elements of writing (e.g. bent linesor straight line sections) even when they are interrupted. Thus, theinventive concept of detecting characters is not considerably impairedeven by poor reproduction of characters in the image.

In addition, it shall be noted that by using a Hough transformer,documents prepared by means of typewriters may also be processed in aparticularly reliable manner since elements of writing may be reliablydetected, by the Hough transformer, even if there are interrupted lines.Especially with texts prepared using typewriters, it happens thatindividual types of typewriters are unevenly worn, and that, e.g., somelines are interrupted.

Thus, two essential advantages are achieved by utilizing a Houghtransformer for detecting characters. On the one hand, the informationprovided by the Hough transformer about detected elements of writing isparticularly reliable and meaningful information which enables efficientdatabase comparison. On the other hand, any disturbances in thecharacters, e.g. interruptions of a line of writing, are essentiallyoffset by utilizing a Hough transformer, so that reliable detection ofwriting is possible even in the event of poor originals.

In an advantageous embodiment of the present invention, the Houghtransformer is implemented to identify both circular arcs or ellipticalarcs and straight line sections as identified elements of writing. Inthis case, the character description generator is advantageouslyimplemented to utilize both the identified circular arcs or ellipticalarcs and the identified straight line sections for detection of writing.

Such an embodiment of the inventive device for detecting characters willbe particularly advantageous if the writing to be detected contains botharcs and straight line sections.

However, in cases where specific writings, or fonts, contain only arcsor straight line sections (which is the case with some computer fonts),detection of either circular arcs or straight line sections willsuffice.

In a further advantageous embodiment, the character descriptiongenerator is implemented to obtain, as the character description, adescription of a character which describes the character as an ordereddescription of identified elements of writing. If, thus, the individualelements of writing are made to have a predetermined order in accordancewith a predefined arrangement rule, a database comparison may beperformed in a particularly efficient manner.

In an advantageous embodiment, the character description generator isimplemented to order the character description such that the orderedidentified elements of writing describe a continuous line of writing. Inthis case, the arrangement of the character description corresponds to anatural sequence of a manner in which a character is reproduced by aperson, for example. The corresponding description is typicallyunambiguous, since a person writes down a character in a specificsequence. Thus, the described implementation of the characterdescription generator in turn results in a particularly efficient andtypically unambiguous character description, as a result of which thedatabase comparison performed in the database comparator may beconducted in a highly efficient manner.

In a further advantageous embodiment, the inventive device comprises aline-of-writing detector implemented to identify, on the basis oflocations of the elements of writing identified by the Houghtransformer, a line along which the characters are arranged. It has beenfound, specifically, that characteristic points of the elements ofwriting identified by the Hough transformer (for arcs: e.g. extremepoints; for straight line sections: for example end points) typicallyrun along lines which are characteristic in the typeface. Thus, there isa very simple and efficient possibility of further exploiting theinformation provided by the Hough transformer in order to obtainadditional information about the typeface.

In a further advantageous embodiment, the character descriptiongenerator is implemented to generate the character description such thatthe character description describes information about locations of theidentified elements of writing in relation to at least one detected lineof writing. It has been found, specifically, that locations of theelements of writing in relation to lines of writing (for example a lowerline, a base line, a center line or an upper line of the writing)provides an even more meaningful form of description, so that thereliability of the detection of writing may be improved. The descriptionof the locations of the elements of writing in relation to at least oneline of writing enables the character description to directly expresswhether characters have descenders or ascenders, for example. Using thisinformation, particularly reliable identification of the characters maybe conducted.

In a further advantageous embodiment of the present invention, thedevice comprises a connectivity number calculator implemented tocalculate a Euler connectivity number on the basis of an image contentof an image section of the image which comprises a character.Advantageously, the device will then also comprise a connectivity numberexaminer implemented to compare the Euler connectivity number, which hasbeen calculated for the image section, to a predetermined comparativeconnectivity number which is contained in a database and is associatedwith a character detected in the image section. Thus, reliabilityinformation may be obtained which carries information about thereliability of detecting a character. Thus, some detection errors inwriting detection may be identified by using the Euler connectivitynumber, whereupon a user of the inventive device may be warned, forexample, or whereupon renewed, refined detection of a character may takeplace.

In addition, the present invention provides corresponding methods ofdetecting characters in an image.

Moreover, further advantageous embodiments of the present invention aredefined by the dependent patent claims.

Other features, elements, steps, characteristics and advantages of thepresent invention will become more apparent from the following detaileddescription of preferred embodiments of the present invention withreference to the attached drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the present invention will be detailed subsequentlyreferring to the appended drawings, in which:

FIG. 1 shows a graphical representation of an exemplary raster image andof image sections processed successively;

FIG. 2 a shows a block diagram of an inventive device for determiningcoordinates of an ellipse point in accordance with a second embodimentof the present invention;

FIG. 2 b shows a graphical representation of three exemplary referencecurves for utilization in an inventive pattern detection means;

FIG. 3 a shows a first graphical representation of an exemplary rasterimage having detected bent line segments marked therein;

FIG. 3 b shows a second graphical representation of an exemplary rasterimage having detected bent line segments marked therein;

FIG. 4 shows a block diagram of a pattern detection means forutilization in an inventive device for determining information about ashape and/or a location of an ellipse in a graphic image;

FIG. 5 a shows a graphical representation of a procedure for moving agraphic image through the pattern detection means in accordance withFIG. 4;

FIG. 5 b shows a graphical representation of time signals which resultduring the conversion of a raster image to parallel time signals;

FIG. 6 shows a block diagram of an inventive device for detectingcharacters in an image in accordance with an embodiment of the presentinvention;

FIG. 7 shows a block diagram of an inventive device for detectingcharacters in an image in accordance with an embodiment of the presentinvention;

FIG. 8 a shows a graphical representation of three characters “a” “c”“d”;

FIG. 8 b shows a representation of a character description of thecharacter “a”;

FIG. 9 shows a graphical representation of a sequence of contiguouscharacters and of a lower line, base line, center line and upper linewhich occur within the typeface;

FIG. 10 a shows a graphical representation of a character “a” within aline system consisting of a lower line, a base line, a center line andan upper line;

FIG. 10 b shows a representation of an exemplary description of acharacter;

FIG. 10 c shows a representation of an exemplary description of thecharacter “a” shown in FIG. 10 a;

FIG. 11 shows a block diagram of an inventive character descriptiongenerator in accordance with an embodiment of the present invention;

FIG. 12 shows a block diagram of an inventive character descriptiongenerator in accordance with an embodiment of the present invention;

FIG. 13 shows a graphical representation of a character “g”;

FIG. 14 shows a flow chart of an inventive method of detecting acharacter in an image; and

FIG. 15 shows a graphical representation of extreme points detected inan image; and

DETAILED DESCRIPTION OF THE INVENTION

FIG. 6 shows a block diagram of an inventive device for detectingcharacters in an image. The device according to FIG. 6 is designated by2800 in its entirety. The device 2800 is advantageously implemented toreceive an image 2808. The device 2800 optionally includes an imagepreprocessing 2810 implemented to generate a preprocessed version 2812of the image from the image 2808. The device 2800 further includes aHough transformer 2820 implemented to receive the image 2808 or thepreprocessed version 2812 of the image and to identify elements ofwriting in the image 2808 or in the preprocessed version 2812 of theimage. According to one embodiment of the present invention, the Houghtransformer 2820 is implemented to identify arcs of a circle or arcs ofan ellipse in the image 2808 or in the preprocessed version 2812 of theimage as identified elements of writing.

In another advantageous embodiment, the Hough transformer 2820 isimplemented to identify a plurality of straight line sections runningfrom different directions through the image 2808 or through thepreprocessed version 2812 of the image as the identified elements ofwriting.

In a further embodiment, the Hough transformer 2820 is implemented toidentify both arcs of a circle or arcs of an ellipse on the one hand andalso straight line sections on the other hand in the image 2808 or inthe preprocessed version 2812 of the image, respectively, as identifiedelements of writing. The Hough transformer 2812 is further implementedto provide information 2822 on the identified elements of writing to acharacter description generator 2830.

The character description generator 2830 is implemented to obtain acharacter description 2832 describing a position of the identifiedelements of writing based on the identified elements of writing, i.e.based on the identified arcs of a circle or arcs of an ellipse, and/orbased on the identified straight line sections.

A database comparator 2840 is implemented to receive the characterdescription 2832 from the character description generator 2830 and tocompare the character description 2832 to a plurality of comparativecharacter descriptions 2842. Advantageously, character codes areassociated with the comparative character descriptions 2842, which may,for example, be stored in a database 2844. The database comparator 2840is implemented to provide a character code 2846 of a detected characterbetween the character description and the plurality of comparativecharacter descriptions.

Based on the structural description of the device 2800 above, in thefollowing the functioning of the device 2800 will be explained in moredetail.

In this respect it is to be noted that the Hough transformer 2820 ispreferably implemented to detect different character and/or elements ofwriting, e.g. arcs of a circle and/or arcs of an ellipse and/or straightline sections in the image 2808 or in the preprocessed version 2812 ofthe image. In this respect it is to be noted that a Hough transformer isable to detect straight or bent lines as a whole due to its functioning,even if the lines are interrupted. Here, it is only necessary for theinterruptions of the lines not to be too long. This is achieved by aHough transformer, for example by bending inclined or bent lines into astraight line step by step, wherein the straight line is detected then.A detection of a straight line is typically especially simple, as fordetecting a straight line it only has to be checked how many imagepoints exist along a straight line. If the number of image points alonga straight line is greater than a predefined minimum number, it may beassumed that a straight line exists, even if not all points along thestraight line exist. If, however, less than a predefined number ofpoints along a straight line are present, it may be assumed that no linein present in an image.

A Hough transformer generally speaking is an especially reliable meansto detect also non-continuous lines running along a predefined curve(i.e. for example along an arc of a circle, an arc of an ellipse or aninclined line) as a whole, even if short interruptions exist.

Further, due to its operating principle, a Hough transformer providesinformation at least regarding one location of the identifiedline-shaped elements (arcs of a circle and/or arcs of an ellipse and/orstraight line sections).

The information provided by the Hough transformer typically alsoincludes, in addition to positional information, information about acourse of the identified element of writing, for example informationabout a direction of an identified straight line section or informationabout a curvature radius of an identified arc of a circle or arc of anellipse.

It is further noted that the Hough transformer typically also providesinformation about an extreme point of an arc of a circle or arc of anellipse, i.e. about a point which is located farthest in a certaindirection, in the detection of an arc of a circle or an arc of anellipse.

In summary, it may generally be noted that a Hough transformer providesa plurality of parameters describing a location of individual elementsof writing, wherein elements of writing having short interruptions aredescribed as one single continuous element of writing. Thus, by the useof a Hough transformer, the problem of conventional means for characterdetection is prevented, that, when the slightest interruptions exist inthe typeface, a fragmentation of the characters into a plurality ofindividual components occurs directly. The use of a Hough transformer,on the contrary, brings a substantial measure of insensitivity againstsuch interferences.

The character description generator 2830 thus receives a description ofa very limited number of individual elements of writing from the Houghtransformer (arcs of a circle or arcs of an ellipse on the one handand/or straight line sections on the other hand).

From the limited number of elements of writing identified by the Houghtransformer, either describing arcs of a circle to which a certainsufficient number of image points belong, or describing straight linesections to which a sufficient number of image points belong, thecharacter description generator generates a compact characterdescription describing the identified arcs of a circle or arcs of anellipse. In other words, by the character description generator 2830 anespecially advantageous description of characters is formed, includinglocation parameters and/or further parameters, e.g. curvature parameterswith arcs of a circle or arcs of an ellipse and direction parameterswith straight line sections. Thus, a character is all in all describedby its natural components, i.e. by a sequence of arcs (arcs of acircle/arcs of an ellipse) and/or straight line sections.

The identified basic elements of a font, or writing, thus correspond toa form of description using which a human user might describe acharacter unknown to him in an efficient way. Thus, the characterdescription 2832 provided by the character description generator 2830represents an efficient description of a character existing in the image2808 or in the preprocessed version 2812 of the image, respectively,wherein the description advantageously only includes such elements whichare identified by the Hough transformer 2820.

By an adaptation of the Hough transformer to characteristics ofdifferent fonts, the inventive device 2800 may thus be adapted todifferent fonts in a simple and efficient way. If a font for examplemainly consists of round elements, as it is the case with German scriptor some computer fonts, the Hough transformer 2820 may in particular beadapted to the detection of arcs of a circle of different curvatureradii, whereby in the information 2822 provided by the Hough transformer2820 mainly (or, alternatively, exclusively) a description of arc-shapedelements of writing is contained.

If a font is, however, a font which basically includes straight lines,as is, for example, the case with some computer fonts, the Houghtransformer 2820 may be implemented to advantageously (or,alternatively, exclusively) detect straight lines of differentdirections.

Thus, the character description generator 2830 advantageously containsinformation about the substantial features of the currently processedfont. Thus, the character provider 2830 only has to generate arepresentation of the information 2822 provided by the Hough transformer2820 which may be processed by the database comparator. By this, thecharacter description generator 2833 may be realized with acomparatively low effort.

As the subsequent database comparison via the database comparator 2840is based on a description of the basic elements (arcs of a circle/arcsof an ellipse and/or straight line sections), the comparison may alsotake place in an efficient way. The reason for this is, among others,that typical characters only contain a very limited number ofcharacteristic character elements. Thus, a font may be described byespecially few features, for example by the features and/or parametersof the characteristic elements of writing. A low number of elements tobe used for the database comparison results in a very efficientrealization of the database comparator, whereby the computational powermay be kept low and the character detection may take place very rapidly.

Apart from that it is to be noted that the characters may already benarrowed down extremely by the presence of a certain number of differentelements of writing. In other words, if a number of different elementsof writing is known (arcs of a circle/ellipse and/or straight linesection), only a very limited number of characters are possible. By sucha pre-selection, the database comparison executed by the databasecomparator 2840 may be made substantially more efficient than is usuallythe case.

In summary it may thus be determined, that the device 2800 enablesespecially efficient character detection due to the fact that onlycharacteristic elements of writing are detected by the Houghtransformer, whereby strongly information-compressed information 2822results, based on which an expressive character description 2832 may begenerated with little effort. Thus, a high efficiency results, andfurther a high reliability of the database comparison executed by thedatabase comparator 2840.

Details with regard to the individual means of the device 2800 areexplained more explicitly in the following.

FIG. 7 shows a block diagram of an extended device for detectingcharacters in an image. The device of FIG. 7 is designated by 2900 inits entirety.

The device 2900 is implemented to receive an image 2908 which basicallycorresponds to the image 2808. The device 2900 further includes an imagepreprocessing 2910 which basically corresponds to the imagepreprocessing 2810. The image preprocessing 2910 includes, in aadvantageous embodiment, one or several of the followingfunctionalities: binarization, edge detection, character separation.

The image preprocessing 2910 thus provides a preprocessed version 2912of the image which basically corresponds to the preprocessed version2812 of the image.

It is to be noted that the image preprocessing may, for example, beimplemented to receive the image 2908, convert the same into a graylevel image (as far as the image 2908 is not yet present as a gray levelimage), and then apply a threshold value to the gray level values.Depending on whether a gray level value of an image point is greaterthan or smaller than a default or adaptively set threshold value, anassociated image point in the preprocessed version 2912 of the image isset to a first value and/or color value or to a second value and/orcolor value. Thus, for example from the image 2908 an associatedmonochrome image results.

In a advantageous embodiment, the threshold value used for binarizationis set depending on a histogram distribution of gray levels in the image2908 and/or in a gray level version of the image 2908. In anotherembodiment, however, also a fixedly predefined threshold value may beused. If a new image is recorded, in a advantageous embodiment thethreshold value used for binarization is readjusted.

It is further to be noted that a binarization may in a further,advantageous embodiment be executed without an intermediate step ofconverting the image 2908 into a gray level image, if, for example,threshold values are directly applied to the different colorintensities.

In a further advantageous embodiment, the image preprocessing 2910 forexample includes an edge detection in addition to binarization. By theedge detection, for example edges in the monochrome image generated bythe binarization are detected. In other words, transitions between thetwo colors in the monochrome image are e.g. marked as edges. This isespecially advantageous, as a Hough transformer may deal especially wellwith an edge image.

Apart from that, it is to be noted that the edge detection may also takeplace directly using the image 2908, i.e., for example without the useof a binarization.

In a further, advantageous embodiment, the image preprocessing 2910further includes a character separation. Here, individual characters areseparated. If, for example, different identified edge comprise adistance which is greater than a predefined distance, it is, forexample, assumed that two separate characters exist. It is, for example,advantageous when characters are in principle separated from each otherby a minimum distance. Thus, by a character separation, for example fromone image a plurality of image sections results, wherein each imagesection advantageously only includes one individual character.

It is to be noted that different approaches exist for characterseparation which are not to be explained in detail here.

All in all, by image preprocessing 2910 thus a preprocessed version 2912of the image 2909 results. The device 2900 further includes a Houghtransformer 2920. The Hough transformer 2920 fulfils the same functionas the Hough transformer 2820 which was described with reference to FIG.28. Thus, at the output of the Hough transformer 2920 information aboutidentified elements of writing is available, wherein the identifiedelements of writing may be arcs of a circle, arcs of an ellipse and/orstraight line sections.

The device 2900 further includes a line-of-writing detector 2926. Theline-of-writing detector 2926 receives the information 2922 aboutidentified elements of writing provided by the Hough transformer 2920and is implemented to provide information 2928 about lines of writing inthe image 2908 or, respectively, in the preprocessed version 2912 of theimage.

The line-of-writing detector 2926 is here implemented to detect, basedon the information 2922 on identified elements of writing provided bythe Hough transformer 2920, lines in the image, on which an excessivelylarge number of extremes of arcs of a circle or arcs of an ellipse arelocated and/or on which an especially large number of straight linesections end.

Details with regard to the functionality of the line-of-writing detector2926 are described later with reference to FIGS. 31 and 32.

Apart from that, it is to be noted that the line-of-writing detector2926 may optionally also be omitted.

The device 2900 further includes a character description generator 2930which in its function basically corresponds to the character descriptiongenerator 2830. The character description generator 2930 is, however, inone preferred embodiment, in which the line-of-writing detector 2926 ispresent, configured to use both information 2928 about lines of writingin the image provided by the line-of-writing detector 2926 and alsoinformation 2922 on identified elements of writing provided by the Houghtransformer 2920 in order to generate a character description 2932.

The character description generator 2930 is here advantageouslyimplemented to generate the character description 2932 so that thecharacter description 2932 describes a relative position of elements ofwriting described by the information 2922 with regard to the lines ofwriting described by the information 2928.

Thus, an especially advantageous character description 2932 results, inwhich the lines of writing to be described in more detail areconsidered. The corresponding character description 2932 which considersinformation about the lines of writing 2928, and which indicates theparameters of the identified elements of writing 2922, advantageously inrelation to the identified lines of writing, is thus insensitive withregard to a rotation or a dimensional scaling of the characters.

The device 2900 further includes a database comparator 2940 whichreceives the character description 2932 and with regard to its functionbasically corresponds to the database comparator 2840 of the device2800. The database comparator 2940 is thus advantageously coupled to adatabase 2944 to receive comparative characters 2942. The databasecomparator 2940 apart from that provides a character code 2946 of adetected character.

In an advantageous embodiment, the device 2900 further includes anoptional means 2958 for checking the reliability of an identifiedcharacter. The means 2958 for checking the reliability of an identifiedcharacter includes a Euler connectivity number calculator 2960. TheEuler connectivity number calculator 2960 either receives the image 2908or the preprocessed version 2912 of the image and thus provides Eulerconnectivity number information 2962 including a Euler connectivitynumber of an image content of the image 2908 or the preprocessed version2912 of the image. The Euler connectivity number is, moreover, sometimesreferred to as the Euler characteristic in the literature and describesa difference between a number of objects in the image (or in thepreprocessed version of the image) and a number of holes or enclosedareas in the image. Further details with regard to the calculation ofthe Euler connectivity number which is executed by the Eulerconnectivity number calculator 2960 are to be described in thefollowing.

The device 2958 for determining the reliability of the characterdetection further includes a character examiner 2970 coupled to theEuler connectivity number calculator 2960 to receive the Eulerconnectivity number 2962. The character examiner 2970 is further coupledto the database comparator 2940 to obtain a comparative Eulerconnectivity number 2972 belonging to a detected character. Thecomparative Euler connectivity number 2972 is here provided by thedatabase comparator 2940 based on an entry in the database. Thecharacter examiner 2970 is further implemented to provide characterreliability information 2974. Here, the character examiner 2970 isadvantageously implemented to indicate the high reliability of adetected character when the actual Euler connectivity number 2962determined by the Euler connectivity number calculator 2960 from theimage 2908 or from the preprocessed version 2912 of the image,respectively, corresponds to the comparative Euler connectivity number2927 taken from the database 2944 which belongs to an identifiedcharacter. In contrast to that, by the character reliability information2974, the character examiner 2970 advantageously indicates a lowreliability of an identified character when a deviation between theactual Euler connectivity number 2962 and the comparative Eulerconnectivity number 2972 exists.

In the following, a procedure in the detection of characters isexplained with reference to FIGS. 30 a, 30, 31, 32 a, 32 b, 32 c, 33, 34and 35.

FIG. 8 a in this respect shows a graphical representation of threecharacters “a” “c” “d”. In the characters “a” “c” “d” here, for example,extreme points of arcs of a circle or arcs of an ellipse, respectively,are indicated as well as center points of straight line sections. Thementioned points are designated by “x”. It is to be noted that anextreme point of an arc is a point of the arc which is farthest in apredefined direction. If it is assumed that the characters are plottedin an (e.g. rectangular) x-y coordinate system, then the extreme pointsof arcs are, for example, points of the arcs which are farthest in thepositive x direction, negative x direction, positive y direction andnegative y direction. An x-y coordinate system is, moreover, designatedby 3010 in FIG. 8.

Further, an extreme point of a first (upper) arc of the letter “a” isdesignated by 3620. An extreme point of a second left arc is designatedby 3622. An extreme point of a third, lower arc is designated by 3624. Acenter point of a first straight line section is designated by 3626, anda center point of a second straight line section is designated by 3628.It is to be noted that an arc is a section of an at least approximatelycircular or ellipse-shaped line. In other words, the Hough transformerdetects that a course of line of the character “a” is approximated in anenvironment of the first extreme point 3620 by an arc of a circle or anarc of an ellipse, and that further a course of line of the letter “a”is, for example, approximated in an environment of the line center point3626 by a straight line section.

Just like with the letter “a”, also for letters “c” and “d”corresponding extreme points of approximation circular arcs and/orapproximation elliptical arcs as well as center points of approximationline sections are marked by an “x”.

FIG. 8 b shows a tabular illustration of a simple description of letter“a”. Here, it is assumed that the Hough transformer 2830 of the device2800 and/or the Hough transformer 2930 of the device 2900 may, forexample, identify a location of an extreme point of different curvedlines and may further identify a location of a center point of differentcurved lines.

Thus, for letter “a”, according to FIG. 8 a three arcs and two straightline sections are identified. The description of letter “a” according toFIG. 30 b thus includes a description for the three arcs and for the twostraight line sections. The first arc around the extreme point 3620 isan arc that is curved downward, so that the associated description, forexample, includes an attribute and/or a parameter indicating a downwardcurvature. Further, the description advantageously includes informationabout a position of the extreme point 3620. The position mayadvantageously be indicated by associated coordinates x, y. Thedescription of the first arc around the extreme point 3620 furtheroptionally includes an indication of a curvature radius r of the firstarc.

Similarly, a description of a second arc approximating character “a” ina surrounding of the second extreme point 3622 includes informationabout the fact that the arc comprises a curvature to the right. Thedescription of the second arc may again indicate a position of thesecond extreme point 3622 in the form of coordinates x, y and optionallyinformation about a curvature radius r of the second arc. Acorresponding description may also be given for the third arcapproximating the letter “a” in a surrounding of the third extreme point3624, as is illustrated in FIG. 30 b. It is to be noted, however, thatthe arcs approximating letter “a” at the extreme points 3620, 3622, 3624may also be described by other parameters.

Apart from that it is to be noted that an extreme is advantageously alocal extreme which needs not necessarily be a global extreme.

For the two straight line sections of letter “a”, for example by theHough transformer 2820, 2920, information about a direction may beprovided. Thus, the description of the first straight line section withthe center point 3626 may, for example, indicate that the first straightline section goes to the top right. The description may furtheroptionally indicate the angle under which the line section is inclinedas compared to a horizontal. Further, the description may, for example,indicate the position of the center point 3626 of the corresponding linesection by the coordinates x, y. Further, the description of the firststraight line section may include information about the length of thefirst straight line section in the form of a parameter 1. Alternatively,however, also other parameters may be used for the description, forexample the coordinates of a starting point or an end point.

FIG. 8 b shows two descriptions of the straight line sections, the firststraight line section with the center point 3626 and the second straightline section with the center point 3628. Thus, it may be seen from FIG.8 b that, based on the information provided by the Hough transformer3820, 2920, an efficient description of a character may be generated.

In the following it will be described, how the information provided bythe Hough transformer may be further processed for a comparison in thedatabase comparator 2840, 2940, to obtain a more favorable illustrationof the information 2822, 2922 about identified elements of writing. Itis to be noted, however, that, in a simplified embodiment, theinformation provided by the Hough transformer 2820, 2920 may also bedirectly supplied to a database comparator 2840.

FIG. 9 shows a graphical representation of characters in German scriptand of associated lines of writing. The graphical representation of FIG.9 is designated by 3100 in its entirety. The graphical representation3100 shows four characteristic lines of writing, i.e. a lower line 3110,a base line 3120, a center line 3130 and an upper line 3140. Further,the schematic illustration 3100 shows a lettering “abcdefg” 3150 andfour capital letters “ABCJ” 3160. From the graphical representation 3100it may be seen that the lower-case letters “a”, “c” and “e” at leastideally lie between the base line and the center line and touch theselines. The lower-case letters “b” and “d” as well as the capital letters“A”, “B” and “C” lie between the base line 3120 and the upper line 3140and typically touch the base line 3120 and the upper line 3140. Theletter “f”, however, lies between the lower line and the upper line andtouches the lower line 3110 and the upper line 3140. The same holds truefor the upper case letter “J”.

In the graphical representation 3100, apart from that extreme points ofarcs contained in the letters (e.g. of arcs of a circle or of anellipse) are marked with an “x”. Further, the end points of straightline sections are marked with “⊕”. From the schematic illustration 3100,it may be seen that along the lines of writing 3110, 3120, 3130, 3140 anespecially large number of extreme points of arcs occur. Further, thereis an especially large number of end points of straight line sections onthe lines of writing. It is thus understandable that a line-of-writingdetector 2926 may detect a line of writing by searching for a straightline on which there is an especially large number of extreme points ofarcs and/or an especially large number of end points of straight linesections. For determining the lines of writing, the line-of-writingdetector 2926 may thus advantageously use the information 2922 providedby the Hough transformer 2960 regarding extreme points of arcs and/orthe information regarding end points of a straight line sectionsprovided by the Hough transformer 2920. Based on the mentionedinformation, the line-of-writing detector 2926 determines lines alongwhich an accumulation of extreme points of arcs and/or of end points ofstraight line sections occurs.

Thus, the line-of-writing detector 2926 for example provides informationabout a position of the lines of writing 3110, 3120, 3130, 3140, i.e.,for example, about a position and/or a direction of the mentioned linesof writing as information 2928.

The line-of-writing detector may, apart from that, be implemented toadditionally use a pre-knowledge about the position of the lines ofwriting with regard to each other (for example with regard to the factthat the lines of writing are parallel to each other and have certainrelative spacings) to determine the lines of writing.

In the following, it is described with reference to FIGS. 10 a, 10 b and10 c how an especially advantageous character description may bedetermined by describing the position of the extremes relative to thedetermined lines of writing. For this purpose, FIG. 10 a shows agraphical representation of a character “a” in a line system consistingof a lower line, a base line, a center line and an upper line. Thegraphical representation according to FIG. 10 a is designated by 3200 inits entirety. The graphical representation 3200 shows a lower line 3210,a base line 3212, a center line 3214 and an upper line 3216. A character“a” is here arranged between a base line and a center line. Thecharacter “a” is designated by 3220 in its entirety. A first, top arc ofthe character “a” comprises an extreme 3230. The extreme 3230 in theexample of FIG. 10 a is located on the center line 3214. It may beassumed here, that the extreme 3230 is at least approximately located onthe center line and/or is regarded to be located on the center line,when a distance of the extreme 3230 from the center line is smaller thana predefined threshold which is indicated either absolutely (in terms ofan absolute distance) or relatively (depending on a distance between twobase lines). Here, for example, basically arbitrarily an x coordinate(x=0) is associated with the extreme 3230 of the first arc.

The character “a” includes a second, left arc whose extreme 3232 liesbetween the base line 3212 and the center line 3214. Further, an xcoordinate (x=−0.5) is associated with the second extreme 3232,describing a position (or a horizontal position or x position,respectively) relative to the first extreme 3230 selected as a referencepoint. The character “a” further includes a third, bottom arc whoseextreme 3234 is located on the base line 3212. In other words, adistance of the third extreme 3234 from the base line 3212 is smallerthan a predefined threshold, wherein the predefined threshold may againbe determined and/or predefined as an absolute value or as a relativevalue (depending on a distance between two lines of writing).

Further, also the third extreme 3232 comprises an x coordinatedescribing a position relative to the reference point. In theillustrated example, the following applies: x=−0.1.

Similar information may, moreover, also be given for the furthercomponents of character “a” (and for all other characters), for examplefor the right approximately straight line section of the character “a”.

It is further to be noted that for example a range between the lowerline 3210 and the base line 3212 may be defined as a first interval(interval I). A range between the base line 3212 and the center line3214 may further be defined as a second interval (interval II). A rangebetween the center line 3214 and the upper line 3216 may further bedefined as a third interval (interval III).

It is further to be noted that, depending on a distance of the lines ofwriting confining the interval, a value between 0 and 1 may beassociated with a position within an interval, wherein the valuedescribes a vertical position or a relative y position, respectively. Inother words, if a certain point (e.g. an extreme point of an arc, astarting point of a straight line section, a center point of a straightline section or an end point of a straight line section) is locatedwithin an interval, a relative position coordinate of approximately 0may, for example, be associated with this point when the point islocated at the beginning of an interval, and further a relative positioncoordinate of approximately 1 may be associated with the point, when thepoint is, for example, located close to the end of the interval. If, forexample, a corresponding point lies between the base line and the centerline, but very close to the base, then, for example, a locationparameter of zero is associated with the point. If the point liesbetween the base line and the center line, however, but close to thecenter line, for example a location parameter of approximately 1 may beassociated with the point. Apart from that, for example a locationparameter of 0.5 may be associated with a point which is located in themiddle between the base line and the center line. However, one need notnecessarily select a linear association of the location parameters. Alsoa boundary of the location parameters to an interval between 0 and 1need not be used, but is only to be regarded as an example. Rather, itis a general advantage in one embodiment of the present invention thatrelative location parameters are associated with points (extreme pointsof arcs, starting points of straight line sections, end points ofstraight line sections or center points of straight line sections) whichare related to the base lines.

With reference to FIG. 10 b, in the following an exemplary form ofdescription for a character is given. For an arc (i.e., for example, anarc of a circle or an arc of an ellipse) the description may compriseinformation about the direction which indicates whether the arc iscurved upward, downward, to the left or to the right. Further, thedescription for an arc may include information about a position of anextreme. The position of the extreme may, for example, be indicated asan absolute or relative position in a first direction (for example in adirection along the lines of writing, which is also designated as the xdirection). Alternatively, however, also relative information about aposition in the x direction may be given, for example indicating aposition with regard to a point of comparison in the character. Thepoint of comparison is, for example, an arc, a starting point of astraight line section, an end point of a straight line section or acenter point of a straight line section. Likewise, the reference pointmay, however, also be an otherwise selected characteristic point of acharacter, for example a center point, a point which is farthest in acertain direction or another characteristic point. Further, thedescription of an arc advantageously includes information about aposition y relative to the lines of writing 3210, 3212, 3214, 3216. Theinformation about the position relative to the lines of writing may, forexample, indicate whether the point in an interval is located betweentwo lines of writing (within a predefined tolerance) or on a line ofwriting. Further, the information may optionally indicate in whatinterval between the lines of writing the point is given.

Additionally or alternatively, the information about the positionrelative to the lines of writing may contain formation about whether apoint (for example within a predefined tolerance range) lies on one ofthe lines of writing and, if yes, on which of the lines of writing thepoint is located. Additionally, optionally a more accurate descriptionof the position in the y direction relative to the lines of writing maybe given, for example in the form of a value between 0 and 1, as wasdescribed above. Also the position in the x direction may, apart fromthat, be indicated absolutely or relatively with regard to one or tworeference points.

The information about an arc may further include information about acurvature radius of the arc. The information about the curvature radiusis, however, to be regarded as optional.

Further, the description of a character may include information about astraight line section. For example, the direction of a straight linesection may be contained in the information about a character. Thus, thedirection of a straight line section may, for example, be given by theindication of an angle with regard to a horizontal and/or with regard toa line of writing 3210, 3212, 3214, 3216. Alternatively or additionally,the location of the identified straight line sections may, for example,be given by the fact that the position of a starting point of thestraight line section and of an end point of the straight line sectionis described. As an alternative to that, information about a straightline section may further include information about a length of thestraight line section and about a center point of the straight linesection. The mentioned information regarding the straight line sectionis advantageously selected relative to the lines of writing.

It has thus to be noted that the character description generator 2930may provide the relative description of a character as the information2932 using the information provided by the base line detector 2926 andfurther using the information provided by the Hough transformer 2920, sothat the information 2932 describes the position of arcs and/or straightline sections in the character relative to the lines of writing.

FIG. 10 c shows an exemplary description of the character “a”illustrated in FIG. 10 a. The character “a”, according to thedescription 3280 illustrated in FIG. 10 c, includes a first arc which iscurved downward, whose extreme 3230 comprises the x coordinate x=0 andwhich is further located on the center line 3216. The character “a”further includes a second arc which is curved and/or bent to the right,an whose extreme comprises the x coordinate x=−0.5. The extreme 3232 ofthe second arc is, moreover, located in the second interval (intervalII). A y position of the extreme 3232 may, for example, be indicatedaccurately as y=0.5, whereby it is expressed that the extreme 3232 islocated in the middle between the base line and the center line. Thecharacter “a” additionally includes a third arc which is curved upwardand whose extreme comprises the x coordinate x=−0.1. The extreme 3234 ofthe third arc is located on the base line 3212, moreover. The rightstraight line section of character “a”, according to the description3280, goes to the top right and comprises an angle of 80° with regard toa horizontal and/or with regard to a line of writing. A starting pointof the straight line section comprises an x coordinate of x=0.4 and islocated in the second interval, i.e. between the base line and thecenter line. An end point of the straight line section comprises an xcoordinate of 0.5 and is, for example, located on the center line 3214.The corresponding location information may, for example, be provided bythe character description generator 2930 to the database comparator 2940in an encoded and ordered, or sequenced, manner.

In the following it is described with reference to FIGS. 11 and 12 how,by the character description generator (i.e., for example, by thecharacter description generator 2830 or by the character descriptiongenerator 2930), information 2822, 2932 may be gained which isespecially suitable for being processed by the database comparator 2840,2940.

For this purpose, FIG. 11 shows a block diagram of an inventivecharacter description generator according to an embodiment of thepresent invention. The character description generator according to FIG.11 is designated by 3300 in its entirety. The character descriptiongenerator 3300 which, for example, corresponds to the characterdescription generator 2830 according to FIG. 6 or the characterdescription generator 2930 according to FIG. 7 is implemented to receiveinformation 3322 about identified elements of writing, i.e., forexample, about identified arcs of a circle or about identified arcs ofan ellipse or about identified straight line sections. The information3322 may here, for example, correspond to the information 2822 or theinformation 2922. However, in an alternative embodiment, the information3322 may also be formed based on the information 2928 about lines ofwriting and based on the information 2922 about the identified elementsof writing, and thus describe the position of the identified elements ofwriting relative to the lines of writing. According to one embodiment ofthe present invention it is here only of importance that the information3322 describes different element of writing in one (certain, determinedby the preceding processing) order by parameters. The characterdescription generator 3330 includes an optional element-of-writingselector 3340 implemented to select a real subset of selected elementsof writing from the entirety of elements of writing described by theinformation 3322. The number of elements of writing to be selected mayhere, for example, be given externally. Further, the selection may, forexample, take place randomly. Alternatively, it may also be predefinedthat the element-of-writing selector 3340 selects the subset of selectedelements of writing such that the subset of the selected elements ofwriting describes, for example, a first predefined number of arcs andfurther a second predefined number of straight line sections. Theselection may thus take place randomly or according to a predefinedrule.

An element-of-writing combination generator 3350 is implemented toreceive a description of the subset 3342 of characters selected by theelement-of-writing selector 3340. As an alternative, theelement-of-writing combination generator 3350 may also be implemented,for example, to receive the information 3322 when the element-of-writingselector, for example, is omitted.

The element-of-writing combination generator 3350 is implemented togenerate different combinations of elements of writing described by theinformation 3342 or the information 3322, respectively, and output thesame as information 3352. The information 3352 here, for example,corresponds to the character description 2832 or the characterdescription 2932 and is, for example, supplied to the databasecomparator 2840, 2950.

Different combinations of elements of writing here are a differentarrangement of the elements of writing. If, for example, the information3322 describes three arcs and one straight line section with associatedlocation parameters, then the element-of-writing combination generator3350 may, for example, generate different combinations. A firstcombination, for example, describes the elements of writing in the orderarc 1, arc 2, arc 3, straight line section 1. A second combination, forexample, describes the elements of writing described by the information3322 in the order which is different to the one above: arc 1, arc 3, arc2, straight line section 1. A third combination generated by thecharacter combination generator 3350 describes, for example, theelements of writing described by the information 3122 in a further orderand/or arrangement: arc 1, straight line section 1, arc 2, arc 3.

In other words, the element-of-writing combination generator 3350 isimplemented to form sets of differently ordered elements of writingbased on the information 3322 in which the elements of writing arearranged in a different order. In what order the elements of writing arearranged may, for example, be determined by rules contained in theelement-of-writing combination generator 3350. Alternatively, theelement-of-writing combination generator 3350 may also be implemented touse any possible orders.

Thus, the element-of-writing combination generator 3350 is all in allimplemented to form several differently ordered sets of elements ofwriting described by associated parameters based on one single set ofelements of writing. Thus, the database comparison executed subsequentlyby the database comparator 2840, 2940 is independent of the order inwhich the individual elements of writing are described in theinformation 3322. Accordingly, the identification of a character isindependent of the sequence of the description of the elements ofwriting in the information 3322, which is how an especially reliablewriting detection is achieved.

FIG. 12 shows a block diagram of an inventive character descriptiongenerator according to an embodiment of the present invention. Thecharacter description generator according to FIG. 12 is designated by3400 in its entirety. In this respect, it is to be noted that thecharacter description generator 3400 receives information 3422 aboutidentified elements of writing which basically corresponds to theinformation 3322. In the embodiment illustrated in FIG. 12, theinformation 3422 includes information about, for example, five arcs andtwo straight line sections to which respective location parameters areassociated. One example of the information 3422 is described in moredetail in the following with reference to FIG. 13.

The character description generator 3430, which may, for example, takeover the position of the character description generator 2830 or theposition of the character description generator 2930 or which mayalternatively also be a part of the character description generator2830, 2930, includes an element-of-writing ordering means 3440. Theelement-of-writing ordering means 3440 is implemented, for example, togenerate information 3452 based on the information 3422, in which theelements of writing are ordered such that they describe a continuousbase line in the order reproduced by the information 3452. In otherwords, the element-of-writing ordering means 3440 is implemented toidentify a subsequent element of writing for a certain element ofwriting so that the certain element of writing and the subsequentelement of writing form a continuous line of writing. Thus, theelement-of-writing ordering means 3440 may be implemented, for example,to identify a distance between end points of several elements of writingand to detect two elements of writing as being subsequent elements ofwriting, while a distance between an end point of a first element ofwriting and a starting point of a second element of writing is smallerthan a predefined bound. Thus, the element-of-writing ordering means3440 all in all provides ordered information 3452 which may, forexample, serve as input information for the database comparator 2840,2940.

In the following, with reference to FIG. 13, a concrete example isillustrated, using which the functioning of the element-of-writingordering means 2440 is better understandable. FIG. 13 shows a graphicalrepresentation of a character “g” and of the elements of writing ofwhich the character “g” consists. The graphical representation accordingto FIG. 13 is designated by 3500 in its entirety. The graphicalrepresentation 3500 shows the character “g”. The character “g” includesa first arc 3510, a second arc 3512, a third arc 3514, a fourth arc 3516and a fifth arc 3518. The character “g” further includes a firststraight line section 3520 and a second straight line section 3522. Itmay further be seen that, when writing the character “g”, the individualelements of writing are passed in the following order: arc 1, arc 2, arc3, straight line section 1, arc 4, arc 5, straight line section 2. Thus,for example an end point of the “arc 1” element of writing is adjacentto a starting point of the “arc 2” element of writing. Further, an endpoint of the “arc 2” element of writing is adjacent to one startingpoint of the “arc 3” element of writing. Corresponding relationshipsalso hold true for the starting points and end points of the remainingelements of writing. An end point of the “arc 3” element of writing isfar away from a starting point of the “arc 4” element of writing,however. Based on the above explained circumstances, for example theelement-of-writing ordering means 3410, based on a descriptiondescribing, for example, first the arcs and only then the straight linesections, by reordering generates a description describing the elementsof writing in an ordered order, so that by the corresponding order ofthe elements of writing a continuous line of writing is described.

An exemplary, non-ordered description is designated by 3460 in FIG. 12,while an illustration ordered according to the course of a line ofwriting is designated by 3462.

In the following, an inventive method of detecting a character in animage is described. For this purpose, FIG. 14 shows a flowchart of aninventive method of detecting a character in an image. The methodaccording to FIG. 14 is designated by 3600 in its entirety.

In a step 3610 the method 3600 includes Hough transforming an image or apreprocessed version of the image to identify identified elements ofwriting. The method 3600 further includes, in a second step 3620,generating a character description based on the identified elements ofwriting. Further, in a third step 3630, the method 3600 includescomparing the character description to a plurality of comparativecharacter descriptions to which character codes are associated toprovide, as a result of comparing, a character code of the detectedcharacter.

It is further to be noted that the method 3600 may be supplemented byall those steps which were described with regard to the inventiveconcept (i.e. with regard to the inventive devices).

The inventive device or the inventive method may be implemented inhardware or in software. The implementation may be executed on a digitalstorage medium, for example a floppy disc, a CD, a DVD, an ROM, a PROM,an EPROM, an EEPROM or a FLASH memory having electronically readablecontrol signals which may cooperate with a programmable computer systemso that the corresponding method is executed. In general, the presentinvention thus also consists in a computer program product having aprogram code stored on a machine-readable carrier for executing theinventive method, when the computer program product is executed on acomputer. In other words, the invention may also be realized as acomputer program having a program code for executing the inventivemethod, when the computer program is executed on a computer.

In summary, it has to be noted that the present invention describes anespecially efficient method of character detection.

In this respect it is to be noted that reading town signs, town names orspeed limits is an important aspect of writing detection. Thus, forexample many characters and numbers (in some font types even allcharacters and numbers) are put together from vertical lines, horizontallines, concave lines and convex lines. At least, however, the charactersand numbers in a plurality of font types include vertical lines,horizontal lines, concave lines and convex lines.

For example, a letter “S” has four extremes, just like a letter “O”and/or “o”. The letter “S” is, however, different from the letter “O”with regard to a curvature and to a relative distance of the extremeswith regard to each other.

Based on the mentioned finding, thus a character detection system may bedeveloped. For the detection of diagonal lines in the letters “W”, “Z”,“A”, “K”, “Y”, “X”, “V”, “N” and/or “M”, advantageously a Hubel-Wieselbar detector may be used, as is, for example, described in thepublication “A neural net for 2D-slope and sinusoidal shape detection”by A. Brückmann, F. Klevenz and A. Wünsche (published in: InternationalScientific Journal of Computing, Vol. 3, Edition 1, Ukraine, 2004, pp.21-25). Both the detection of arcs (for example of arcs of a circle orof an ellipse) and also the detection of straight line sections (e.g. ofdiagonal lines) may in one embodiment of the present invention be basedon the same architecture “Dual Hough IP Core”, only with a differentprogramming. In other words, a re-configurable Hough transformer may beused which, for example, in a first configuration state is able todetect arcs and which in a second configuration state is able to detectstraight lines.

In one embodiment of the present invention, a position of found extremesis associated with a line system, for example like in an exercise bookof first-grade students.

Thus, in one embodiment of the present invention, a base line, a centerline, for example with lower-case letters “d”, “c”, and an upper line,for example with upper-case letters “C”, “K”, are determined.

In one embodiment of the present invention, not only the extremes belongto a character detection, but every extreme comprises information“convex/concave top/bottom”, “convex/concave left/right”. In otherwords, in one advantageous embodiment, for example also informationabout a curvature of arcs is determined and assessed later.

In a software simulation (or generally when executing a Hough transform,respectively) for example a range between a maximum curvature radius anda minimum curvature radius may be set, wherein for example only arcshaving a curvature radius between the maximum curvature radius and theminimum curvature radius are detected by the Hough transformer.

Further, in software simulation, or when executing the Hough transform,respectively, with the help of a parameter “DelayLines not sum” it maybe determined which delay lines in the Hough transformer and/or in theHough field do not contribute to a summation. Thus, it may, for example,be finely set that lines around the extreme do not contribute to asummation, as otherwise too often straight lines would be counted and/ordetermined.

In other words, it may be achieved in the Hough transformer that asection of a line which is located in the proximity of an extreme of thecurved line does not contribute to a result of the Hough transform.

Thus, in one embodiment of the present invention, with regard to acurved line in the image (for example with regard to a circularly curvedline or an elliptically curved line) a feature vector results having theform (x, y position of the extremes; curvature radius in a range ofvalues of negative max_curvature radius, positive max_curvature radius).In other words, a feature vector with regard to a curve in the imagedescribes the position of an extreme point of the arc as well as acurvature radius, whereas the curvature radius is smaller regarding itsabsolute value than a maximum curvature radius.

Further, character single segmentation algorithms exist. In thisrespect, reference is made, for example, to documents of K. H. Noffz andR. Lay at the University Ruprecht Karl of Heidelberg or by T. Roska atthe Pázmány P. Catholic University of Budapest.

As an example it is to be noted here that a character “c” includes threeextremes, one on the center line, one on the base line and one inbetween. In this case, a relative x position of the extremes with regardto each other further counts.

In one advantageous embodiment, a classification is executed accordingto a classical variation method of min (X-X_(i))². Alternatively oradditionally, a classification is further performed according to alabeling method by V. Ferrari. For details in this respect, reference ismade to the publication “Object detection by contour segment networks”by V. Ferrari et al. (published: European Conference of Computer vision(ECCV), Graz, May 2006).

For a double cross check or at least for checking the characterdetection, in one embodiment of the present invention the use of a Eulerconnectivity number is obvious. The Euler connectivity number is definedas follows:connectivity number=number of the objects−number of enclosed holes.

An object is here defined as a continuous area of image points, orpixels.

The connectivity number is calculated in a pixel grid from thedetermination of 2×2 search masks according to

$K = {{n*\underset{\lbrack\begin{matrix}0 & 0\end{matrix}\rbrack}{\begin{bmatrix}1 & 0\end{bmatrix}}} - {m*\underset{\lbrack\begin{matrix}1 & 0\end{matrix}\rbrack}{\begin{bmatrix}? & 1\end{bmatrix}}}}$

For the different letters of the Latin alphabet, the following applies:

for “B”: K=−1;

for “Q”, “R”, “O”, “A”: K=0;

for the remaining letters or for the rest: K=1.

Further, FIG. 15 shows a screen illustration of a hyperfine structure ofa writing detection using a WinDelay program.

The graphical representation of FIG. 15 is designated by 3700 in itsentirety. From the graphical representation 3700 it may be seen that acomma at the bottom left is marked (for example by the WinDelayprogram). Further, alternatively or additionally, a letter “G”, a letter“S” and/or a letter “O” is marked. Even a letter thickness and serifsmay be determined. An input image from which the graphicalrepresentation 3700 is gained is a text recorded by a screen with acamera (e.g. using a Web-Cam).

In the following, the method of character detection is again describedstep by step with reference to an embodiment. In the first steps, themethod is similar to an identification of ellipses (also referred to asellipse finder). Parameters, like for example a threshold, may be set ina program “WinDelayLine”, providing the function of a Hough transform.

The character detection is executed step by step as follows:

1) Record an image with a camera. Alternatively, the image may, forexample, also be generated by a scanner or gained otherwise.

2) Set threshold value; binarization (ought to be adaptive according tohistogram distribution of the gray levels); with video readjust moreoften after x frames or best, optimum threshold for each image; standardimage processing.

3) Find contour; contour finder algorithm is an algorithm set up frommorphological operators, in principle an edge detection algorithm.

4) Hough transform; with the help of a Hough transform (for exampleexecuted by the software “WinDelayLine”), extremes in an image are foundan marked (for example in red). Each extreme is indicated with an x, yposition and additionally has a curvature value. The clouds of markedextremes generated by the Hough transform (also referred to as “redclouds”) may be more or less dense, depending on the variation of theparameter core size, minimum curvature radius, maximum curvature radius,delay not sum. For details in this respect, reference is made to thedissertation “Echtzeitfähige, auf der Hough-Transformation basierendeMethoden der Bildverarbeitung zur Detektion von Ellipsen” by J. Katzmann(dissertation at the University of Ilmenau, Germany, 2005).

5) Classification according to Hamilton's variation calculationaccording to min (integral) (X_i−t_j)². In other words, for example adeviation between a feature vector and a comparative feature vector isminimized.

The algorithm works, for example, as follows for the detection of anellipse:

-   -   Set up a list of all possible four-point pairs; fit an ellipse        for each combination of four, determine the ellipse parameters        and form the deviation of the measurement points from the fitted        ellipse. Set up a decreasing list of the combinations of four        according to the min deviation (or the minimum deviation,        respectively).    -   Step 5) is to be regarded as optional however.

6) Letters and numbers consist of lines and arcs. Here, a law of curvesketching has to be taken to heart: each function may be approximated bya node and a second derivation. This is only true in the digital casewith limitations: vertical and horizontal lines are found, also circlesare not a problem, but with straight lines of different inclination themethod does not work well. Here, for example, in the softwareWinDelayLine or in the Hough transformer, respectively, a straight lineHough finder is used, as is described, for example, in the publication“A neural net for 2D slope and sinusoidal shape detection” by A.Brückmann, F. Klefenz and A. Wünsche (published in: InternationalScientific Journal of Computing 3 (1), pages 21-26, 2004).

-   -   Thus, a complete computational neuro-scientific Hubel-Wiesel        solution of an orientation selectivity is achieved.

7) Form a bunch of templates in a contour description language. Forexample, a contour description language may be used as is described inthe publication “Object detection by contour segment networks” by V.Ferrari et al (published: European conference on computer vision (ECDB),Graz, May 2006).

An exemplary description is given here with reference to character “p”:straight downward line; three extremes with a downward curvature,curvature bending to the left, horn growth bending upward.

Form all four-point combination pairs from a straight element and threearc elements under the compulsory condition that a default for aposition (e.g. position below a center line or above a center line) isto be maintained in tolerance ranges and that the “p” line has to belocated left of the three arcs of a circle. For all letters and numbers(at least for a plurality of letters or numbers), their characteristictemplate is to be generated and all combination pairs are to be matchedwith the template. A minimum is the result.

In other words, four points are determined representing extremes of arcsor characteristic points of straight line sections (e.g. startingpoints, center points or end points), wherein certain default positionrelations are to be maintained (e.g. points above or below a centerline, or relation of the existing curvatures).

Further, alternatively or additionally, a method may be applied, as itis described in the publication “object detection by contour segmentnetworks” by V. Ferrari et al. The corresponding method may besummarized as follows: If an object is put together from lines and arcsof a circle, describe, how the object is put together. The descriptionmay for example be as follows: corner; line upward, y centimeters; linedownwards, x centimeters; curvature arc with curvature radius r. Thistemplate is thereupon shifted in different directions (“crisscross”)across marked points (extremes identified in the image; also referred toas “red points”). Form “Min (templates vector—red point vector)”. Inother words, determine a deviation between a template vector and avector describing features or describing a position of the identifiedextremes, respectively (“red points vector”). Where there is a bestpossible match it is assumed that an object is present.

In the following it is still to be explained how a Euler connectivitynumber may be determined. In this respect, it is noted that LeonardEuler, which used to be on a 10 franks bill was the first to determinethe connectivity number. His scripts are in Latin and his formula is:

The connectivity number K is equal to a number of objects minus a numberof holes.

In a translation of this fact that may be used for an application in acomputer system in a pixel grid, this means:

an object is defined as a continuous area of pixels (also referred to aspixel area). For example, an object may be defined as a continuous pixelarea of black pixels. The continuous area of pixels (pixel area) mayalso contain holes and/or enclosures, for example in the form of whitepixels. A hole is here defined as an enclosure in a border of blackpixels. In other words, a hole is for example a pixel of a first color(a white pixel) which is surrounded by pixels of another color (e.g. ofblack pixels).

In the following it is described how the connectivity number may bedetermined based on local 2×2 operators. For this purpose it is countedhow often a 2×2 pattern

$\quad\begin{pmatrix}1 & 0 \\0 & 0\end{pmatrix}$is present in a pixel image.

It is further counted, how often a 2×2 pattern

$\quad\begin{pmatrix}? & 1 \\1 & 0\end{pmatrix}$is present in the image.

Then, the determined numbers are subtracted from each other and thisnumber (for example the result) indicates the connectivity number.

Applied to letters this means, that capital “B” has the connectivitynumber K=−1, that the letters “a”, “b”, “d”, “e”, “q”, “o”, “p” and “R”comprise the connectivity number K=0, and that the remaining letters(for example of the Latin alphabet) comprise the connectivity number 1.

Alternatively, it may be determined, that in another font illustration,the letters “A”, “D”, “O”, “P”, “Q”, “a”, “b”, “d”, “e”, “g”, “o”, “p”and “q” have the connectivity number 0.

The Euler connectivity number may thus serve as a double crosscheck asto whether or not a correct letter was found.

In summary, it may thus be said that the present invention provides anespecially advantageous concept for character detection.

An inventive device for identifying a traffic sign in an image accordingto an embodiment of the present invention will be described below. It isto be noted that the device is implemented to receive an image. Further,the device includes an optional edge detector. Also, the device includesa Hough transformer. The edge detector and the Hough transformercorrespond to the means described with reference to FIG. 10 with regardto their functioning.

The device further includes at least one shape detector or one ellipsedetector, advantageously however both a shape detector and also anellipse detector. With regard to its function, the shape detectorcorresponds to the shape detector described herein, and the ellipsedetector, with regard to its function, corresponds to the ellipsedetector described herein.

In addition, the device includes a pattern identifier. The patternidentifier or pattern detector, respectively, is implemented to receiveinformation about arcs of a circle or arcs of an ellipse in the imagefrom the Hough transformer or to receive information about straight linesections running through the image from the Hough transformer. Thus, theHough transformer may for example be implemented in the device to onlyprovide information about arcs of a circle or arcs of an ellipse in theimage, or to only provide information about straight line sections inthe image. Alternatively, the Hough transformer may also be able toprovide information both about arcs of a circle or arcs of an ellipse inthe image and also about straight line sections in the image. Adecision, what information the Hough transformer provides, among othersdepends on the fact what information is required by the shape detectorand/or the ellipse detector as well as by the pattern identifier.

The pattern identifier includes an image section selector implemented toselect an image section of the image using the information provided bythe shape detector about a (general) shape detected in the image and/orbased on the information provided by the ellipse detector about anelliptical shape detected in the image. The selection of an imagesection may for example be executed as it was described with referenceto FIG. 15.

Further, the image section selector may optionally be implemented toexecute a mapping of the image section, as it was described withreference to FIG. 15. Further, the image section selector mayalternatively be implemented to determine the image section using amasking. The pattern identifier further includes a writing detectorimplemented to receive the image section or information about the imagesection selected by the image section selector. Further, the writingdetector is implemented to receive the information about arcs of acircle and/or arcs of an ellipse and/or straight line sections in theimage provided by the Hough transformer. Further, the writing detectoris implemented to determine those arcs of a circle and/or arcs of anellipse and/or straight line sections lying in the image sectionselected by the image section selector. Thus, the writing detectorreceives information about selected arcs of a circle and/or arcs of anellipse and/or straight line sections in the image lying in the imagesection. The information about selected arcs of a circle and/or straightline sections received by the writing detector thus describes elementsof writing. Thus, a writing detection may be executed as it wasexplained with reference to FIGS. 6-14.

It is further to be noted that the pattern identifier may also be set updifferently. It is here only decisive that the writing detector all inall contains information about selected arcs of a circle and/or arcs ofan ellipse and/or straight line sections in the image, lying within ageneral (e.g. triangular or rectangular or square) or elliptic formdetected by the shape detector or by the ellipse detector. Theinformation about the selected arcs of a circle and/or arcs of anellipse and/or straight line sections in the image thus takes on theplace of the information about identified elements of writing (see FIG.6). If the writing detector detects a character, then the writingdetector for example provides a character code of a detected character,as it was for example described with reference to FIG. 6. The charactercode provided by the writing detector may for example carry informationabout the fact which traffic sign was detected. In other words, thedetected character code may be part of information describing anidentified traffic sign. If the traffic sign includes more than oneletter and/or more than one character, the writing detector mayoptionally also provide information about a font in a traffic sign whichincludes more than one letter. The corresponding font may then, forexample by a comparison to a database, be used to identify theinformation represented by the traffic sign and/or the traffic signitself.

In summary it may thus be noted that the writing detector is implementedto provide a character code of at least one detected character based oninformation about elements of writing lying within a shape detected bythe shape detector or the ellipse detector, wherein the character codeis advantageously used to determine information about an identifiedtraffic sign.

It may thus be noted that the information provided by the Houghtransformer may be reused repeatedly in the detection of a traffic sign.The information about arcs of a circle and/or arcs of an ellipse and/orstraight line sections identified in the image may on the on hand beused to detect general or elliptical shapes in the image with the helpof a shape detector or with the help of an ellipse detector. Further,the information provided by the Hough transformer may subsequently beused to identify one or several characters in an image section describedby the detected shapes, to thus obtain especially reliable informationabout an identified traffic sign.

In one embodiment, the present invention thus provides an especiallyadvantageous concept for traffic sign detection based on the executionof the Hough transform, wherein the information provided by the Houghtransformer may even be reused repeatedly depending on the embodiment.

A means for performing a parallel Hough transform or for determiningextreme points of bent lines will be described below. Detection ofvarious straight line sections while using a parallel Hough transform isdescribed, incidentally, in the publication “A neural net for 2D-slopeand sinusoidal shape detection” by A. Brückmann, F. Klevenz and A.Wünsche (published in: International Scientific Journal of Computing,Vol. 3, 1^(st) ed., Ukraine, 2004, pp. 21-25).

FIG. 1 shows a graphical representation of an exemplary raster image ofan ellipse. The graphical representation of FIG. 1 is designated by 300in its entirety. What is shown in this context is a raster image 310having a plurality of raster points 312. A raster point may be inactiveor white, as is shown for the raster point 312. A raster point mayfurther be active or black, as is indicated, for example, for the rasterpoint 314 by means of hatching. It shall further be noted that theraster image 310 comprises a plurality of raster lines and a pluralityof raster columns. A raster line in this context summarizes a pluralityof raster points, as is illustrated, for example, by the area 320, whichhas thick borders and describes a raster line. A raster column alsodefines a combination of several raster points. One example of a rastercolumn is shown by the thick-border area 322, which represents a rastercolumn. Raster lines and raster columns are advantageously orthogonal toone another. In addition, it shall be noted that raster lines and rastercolumns may overlap, of course. For example, the raster line 320 and theraster column 322 have a common image point designated by 324. It shallalso be noted that an image or image section may be fully described bothby a plurality of raster lines and by a plurality of raster columns,since obviously each rastered area may be described both by raster linesand by raster columns. It shall also be noted that by definition, theraster image 310 comprises a first raster line, the raster line 320, asecond raster line 330, several further raster lines 332, whichadvantageously are numbered consecutively, and a last raster line 334.Corresponding line numbers are designated by 335. Similarly, the rasterimage 310 comprises a first raster column 322, a second raster column336, further raster columns 338, which advantageously are numberedconsecutively, and a last raster column 340.

The graphical representation 300 further shows an ellipse 350represented by the raster image 310 in the form of active or blackraster points (or image points), the active raster points being markedby hatching.

The graphical representation 300 further shows a first group of rastercolumns which is designated by 360. The first group of raster columnscomprises the first raster column 322, the second raster column 336 andall of the following raster columns up until the seventh raster column362, inclusively. The first group 360 of raster columns thus describes asection of the raster image 310.

The above-mentioned section of the raster image further comprises aplurality of raster lines, which are reduced in length as compared tothe original raster lines due to the restriction in the number ofcolumns. The shortened raster lines, which may arise on account of theselection of an image section, will also be referred to as raster linesfor short in the following.

The second group of raster columns, which is designated by 364, furthercomprises the second raster column 336 and the following raster columnsup to an eighth raster column 366. In other words, seven adjacent rastercolumns are combined into one group of raster columns, respectively,which are provided for shared processing.

Similar grouping may be performed for the raster lines, for example thefirst raster line 320, the second raster line 330 and all of thefollowing raster lines up to the seventh raster line 368 being combinedinto a first group 370 of raster lines. Similarly, a second group ofraster lines comprises the second raster line 330 up to the eighthraster line 372, the second group of raster lines being designated by374.

In this context it shall naturally be noted that a group of raster linesmay comprise any number of raster lines, for example five raster lines,16 raster lines, 32 raster lines, or 64 raster lines. In this context itis only advantageous for the number of raster lines combined into agroup of raster lines to be larger than 2. Analogous considerationsshall also apply to a group of raster columns.

FIG. 2 a shows a block diagram of an inventive device for determiningcoordinates of an ellipse point (e.g. of an extreme point of anelliptical arc segment or of a circular arc segment) in a graphic imagein accordance with a second embodiment of the present invention. Thedevice of FIG. 2 a is designated by 400 in its entirety. The device 400is particularly well suited to process a raster image 310 as is depictedin FIG. 1, as will be explained below.

The device 400 is implemented to receive a rastered image 410. Inaddition, the device 400 is optionally implemented to select an imagesection 414 from the rastered image 410 while using an optionalimage-section selection means 412. A selected image section 414 may bedefined, for example, by a plurality of raster lines and/or a pluralityof raster columns, for example by a group of raster lines or a group ofraster columns, as was described with reference to FIG. 1. The inventivedevice 400 further comprises a pattern detection means 420 implementedto receive the rastered image or the rastered image section 414. Inaddition, the pattern detection means 420 is implemented to establishwhether a course of curve or a bent line segment among a set ofreference courses of curve is contained in the rastered image or imagesection 414.

The courses of curve of the set of reference courses of curve here maybe stored within a memory, for example, for determining the similaritybetween courses of curve contained in the rastered image or imagesection 414 and the reference courses of curve. However, it is possiblefor the structure of the pattern detection means to be implemented todetect whether a course of curve which is sufficiently similar to areference course of curve among the set of reference courses of curve iscontained in the rastered image or image section 414. As referencecourses of curve, use is advantageously made of such courses of curvewhich approximate an ellipse at the first ellipse point, at the secondellipse point, at the third ellipse point, or at the fourth ellipsepoint. Consequently, the pattern detection means is generallyimplemented to detect whether a course of curve which detects an ellipseat the first ellipse point, at the second ellipse point, at the thirdellipse point, or at the fourth ellipse point is contained in therastered image or image section 414.

The pattern detection means 420 is further advantageously implemented toidentify, among the set of reference courses of curve, a course of curvewhich is sufficiently similar to a course of curve contained in therastered image or image section 414, as the first bent line segment, thesecond bent line segment, the third bent line segment, or the fourthbent line segment, depending on which one among the first ellipse point,the second ellipse point, the third ellipse point, and the fourthellipse point is the point where the reference course of curve among theset of reference courses of curve approximates the ellipse.

In addition, the pattern detection means 420 is implemented to determineat least one location parameter—but advantageously two locationparameters and, optionally, a further parameter which describes a courseof curve—of the first line segment, of the second line segment, of thethird line segment, or of the fourth line segment. An optionalcoordinate calculation means 430 may then calculate the coordinates ofthe first ellipse point, of the second ellipse point, and of the thirdellipse point or of the fourth ellipse point from the location of theidentified first bent line segment, of the second bent line segment, ofthe third bent line segment, or of the fourth bent line segment.However, the coordinate calculation means 430 may also be omitted if,for example, the location parameters of the bent line segments which aredetermined by the pattern detection means 420 are already defined suchthat the location parameters directly indicate coordinates of the firstellipse point, of the second ellipse point, and of the third ellipsepoint or of the fourth ellipse point, at which the bent line segmentsadvantageously extend through the specific ellipse points.

It shall further be noted that as reference courses of curve, use isadvantageously made of such courses of curve which approximate anellipse at the first ellipse point, at the second ellipse point, at thethird ellipse point, or at the fourth ellipse point (or in surroundingsof the respective ellipse points). As reference courses of curve, use isadvantageously made of symmetrical bent courses of curve. Also, it isadvantageous to use, as reference courses of curve, for example sectionsfrom circular curves, since circular curves approximate an ellipseparticularly well at the first ellipse point, at the second ellipsepoint, at the third ellipse point, or at the fourth ellipse point.

FIG. 2 b is a graphical representation of two examples of referencecourses of curve for utilization in an inventive pattern detectionmeans. The graphical representation of FIG. 2 b is designated by 450 inits entirety. A first graphical representation 452 describes, in theform of a raster image, a first reference course of curve whichapproximates a section from a circular curve with a first curvatureradius r₁. A second graphical representation 454 describes, in the formof a raster image, a second reference course of curve which approximatesa section from a circular line with a second curvature radius r₂, thesecond curvature radius r₂ being larger than the first curvature radiusr₁. In addition, a third graphical representation 456 depicts, in theform of a raster image, a third reference course of curve, which alsodescribes a section from a circular line having a third curvature radiusr₃. In this context, the third curvature radius r₃ is smaller than thefirst curvature radius r₁. The three graphical representations 452, 454,456 of FIG. 2 b therefore describe three potential reference courses ofcurve for utilization in the pattern detection means 420. In otherwords, the pattern detection means 420 may generally be implemented todetect, in the rastered image or image section 414, the three referencecourses of curve depicted in the graphical representations 452, 454, 456of FIG. 2 b, and to identify them, for example, as a first bent linesegment which approximates the ellipse, which is to be identified, atthe first ellipse point. Moreover, the pattern detection means 420 isadvantageously implemented to describe, by means of location parameters,the location of a reference course of curve detected in the rasteredimage or image section 414, and to make said location parametersavailable to the coordinate calculation means 430, unless theabove-mentioned location parameters directly represent the coordinatesof a first ellipse point at which the known reference course of curveapproximates the ellipse to be identified.

FIG. 3 a shows a first graphical representation of an exemplary rasterimage comprising detected bent line segments marked therein. In thiscontext, it is assumed that the pattern detection means 420 of thedevice 400 of FIG. 2 a is able, for example, to detect, in an image orimage section, the reference courses of curve depicted in the firstgraphical representation 452, in the second graphical representation454, and in the third graphical representation 456. It is also assumedthat the exemplary raster image 310 of FIG. 1 a is supplied to thepattern detection means 420 as a rastered image 414. By way of example,it is also assumed that the raster image 310 of the pattern detectionmeans is supplied either on a line-by-line or column-by-column basis.Assuming that the raster image 310 of the pattern detection means 420 issupplied on a column-by-column basis, starting with the first rastercolumn 322, the pattern detection means 420 may detect, in the rasteredimage, e.g. the third reference course of curve of the graphicalrepresentation 456. In the graphical representation 500 of FIG. 3 a, thecourse of curve which is detected in the process is marked by 510.However, if the raster image 310 of the pattern detection means 420 issupplied on a column-by-column basis, starting with the last rastercolumn 340, the pattern detection means may detect, for example, afurther course of curve which is designated by 520 in the graphicalrepresentation 500.

It shall also be noted in this context that the pattern detection means420 may only have an image section supplied to it which is selected bythe image-section selection means 412. For example, in a firstprocessing step, the pattern detection means 420 may only have a limitedimage section supplied to it which comprises the first group 370 ofraster lines. In other words, an image section comprising the firstraster cell 320 and the adjacent raster lines up to the seventh rasterline 368 may be supplied to the pattern detection means 420 in the firstprocessing step. For example, this image section comprises does notcomprise a course of curve which would match any of the referencecourses of curve shown in the graphical representations 452, 454 and456. Subsequently, in the second processing step, the pattern detectionmeans 420 may have an image section supplied to it which comprises thesecond group 374 of raster lines. In other words, the pattern detectionmeans 420 has the image contents between the second raster line 330 andthe eighth raster line 372, inclusively, supplied to it on acolumn-by-column basis (that is, one column after another), for example.This image section, too, does not comprise a course of curve which wouldmatch the three reference courses of curve of the graphicalrepresentations 452, 454, 456. In a third processing step, the imagecontent may further be supplied to a third group of raster lines of thepattern detection means 420. The third group of raster lines here isdesignated by 380 and comprises the raster lines 3 to 9. The patterndetection means 420 may identify, within this image section, a course ofcurve which corresponds to the third reference course of curve of thegraphical representation 456. The identified course of curve istherefore designated by 510 in the graphical representation 500. Itshall further be noted that in order to improve the resolution, adjacentgroups 370, 374, 380 of raster lines overlap, that is comprise commonraster lines. In this context, it is advantageous for adjacent groups ofraster lines to differ by only one single raster line, i.e. for adjacentgroups of raster lines to be mutually offset by exactly one raster line,as is shown in FIG. 1, for example.

In other words, the device 400 may be implemented to successivelyprocess various image sections which comprise various groups of rasterlines, and to subject them to pattern detection. Thus, pattern detectionmeans may only process a small image section in each case, whichintensely reduces the complexity of pattern detection. In addition, thenumber of reference courses of curve used may be kept small as a result.It shall further be noted that information about a location of the bentline segment which approximates an ellipse at the first ellipse point,at the second ellipse point, at the third ellipse point, or at thefourth ellipse point may be derived from the information indicating theimage section in which the reference course of curve may be identified,i.e. indicating the group 370, 374, 380 of raster lines which is usedfor identifying the reference course of curve. In other words, theinformation stating the image section in which the reference course ofcurve is identified represents a location parameter of the bent linesegment and may thus be used for determining at least one coordinate ofthe first ellipse point, of the second ellipse point, of the thirdellipse point, or of the fourth ellipse point.

Similarly, the first image 310 may also be supplied to the patterndetection means 420 on a line-by-line basis, that is one line afteranother. In this context, several image sections, which comprisedifferent groups 360, 364 of raster columns, may be processed one afteranother. The explanations given with regard to column-by-columnprocessing of a group of raster lines shall apply analogously.

It shall also be noted that FIG. 3 b depicts a second graphicalrepresentation of an exemplarily raster image having detected bent linesegments marked therein. The detected bent line segments, which in thegraphical representation 550 of FIG. 3 b are designated by 560 and 570,here correspond to the reference course of curve depicted in thegraphical representation 454.

It shall also be noted that it is advantageous to supply a raster image310 to the pattern detection means 420 on a column-by-column basis forthe first time, starting with the first raster column 322, and to supplythe raster image 310 to the pattern detection means 420 on acolumn-by-column basis for the second time, starting with the lastraster column 340. In a first run, which starts with the first rastercolumn 322, courses of curve of a first direction of curvature may bedetected, while in the second run, which starts with the last rastercolumn 340, courses of curve having a direction of curvature which isopposite thereto are detected. Similarly, line-by-line processing of theraster image 310 may be performed starting with the first raster line320 at one point and starting with the last raster line 334 at anotherpoint so as to be able to identify, in turn, courses of curve havingdifferent curvature behaviors while using a pattern detection meanswhich is designed only to detect courses of curve having one singlecurvature behavior or one single direction of curvature.

FIG. 4 shows a block diagram of a pattern detection means forutilization in an inventive device 400. The circuit of FIG. 4 isdesignated by 600 in its entirety and describes a so-called “Hougharray” for performing a Hough transform. The pattern detection means400, which implements the coordinate determination means 110, mayadvantageously conduct a search for circular curves having differentradii which describe, with sufficient approximation, an ellipse to beidentified around the extreme points, i.e. around the first ellipsepoint, the second ellipse point, the third ellipse point, or the fourthellipse point. This may be conducted, in a particularly advantageousmanner, by a parallel systolic Hough transform. The Hough transform maybe configured for circular curves, and may be adapted, in this context,to the search for extreme values, i.e. for identifying those pointswhich are located farthest in a specific direction.

FIG. 4 shows a particularly advantageous means for performing a Houghtransform. The means 600 for performing a Hough transform here comprisesa plurality of stages 610 connected in series, by means of which severalsignals 612, 614, 616 are passed on in parallel. For each signal, astage contains either a delay element 620, also designated by A, or abypass 624, also designated by B. In addition, the signals at the outputof a stage are supplied to a summing component 630, also designated byC. The summing component is advantageously implemented to establish howmany signals are active at the same time at the output of the respectivestage. A so-called sum of lines is present at an output 632 of a summingcomponent 630, said sum of lines indicating the number of signals whichare active at the same time at the output of the respective stage. Thesum of lines 632 may then be supplied to a comparator 634, whichcompares the sum of lines 632 with a predefined threshold value. If thesum of lines 632 exceeds the predefined threshold value, this will meanthat at least a predefined number of signals are active at therespective stage. In other words, a “straight line” is present, at leastapproximately, at the respective stage, said straight line beingcharacterized in that at least a predefined number of signals of therespective stage are active at the same time. Subsequently, an outputsignal of the comparator 634 is supplied to a delay element 636. Severaldelay elements 636, each of which is connected to an output of acomparator 634 of a stage 610, are cascade-connected such that theoutput signal of a delay element 636 is supplied to the input of asubsequent delay element 636.

It shall further be noted that the delay elements 620, 636 operate in aclocked manner, so that both the signals 612, 614, 616 and the outputsignals of the comparators 634 are passed on in a clocked manner. Thesignals 612, 614, 616 and the output signals of the comparators 634 arepassed on in parallel, in terms of their structure, and in the samedirection, however the signals 612, 614, 616 being delayed to differentdegrees at the individual stages, depending on whether a delay element620 or a bypass 624 is used for passing on the signal 612, 614, 616 at astage 610. However, it is advantageous that a central signal of theplurality of signals 612, 614, 616 be forwarded, through the pluralityof stages, as fast as the signals from the outputs of the comparators634. Advantageously, the central signal is delayed by the same amount ateach of the stages, and the output signals of the comparators 634 arealso advantageously forwarded through the stages with a constant delay.The central signal is advantageously located approximately half waybetween the first signal 612 and the last signal 614, thus describes araster line in the middle of the image section supplied to the Houghtransform means 600, or is spaced apart from the center of the imagesection by a maximum of 25% of a width of the image section. The widthof the image section is defined by the number of raster lines or rastercolumns which are supplied to the Hough transform means 600 at the sametime.

On the basis of the structural description, the mode of operation of thepattern detection means 600 will be described in more detail below. Itshall be assumed that an image section is supplied to the Houghtransform means 600 in the form of parallel time signals 612, 614, 616.The delay elements 620 or the bypasses 624 are configured such thatdifferent time signals 612, 614, 616 are delayed by various degrees whenthey pass through the individual stages. By switching on delay elements620 or bypasses 624, the delays are set such that a bent course of curve(advantageously a circular bent course of curve) is unbent after passingthrough one stage or several stages 610. In other words, a bent courseof curve in the image section processed by the Hough transform meansresults in that the individual signals 612, 614, 616 are active atdifferent points in time. However, suitably setting the delay elements620 or the bypasses 624 may achieve that signals 612, 614, 616 passthrough the individual stages at different speeds, so that ideally, anyforwarded signals which are based on the signals 612, 614, 616 will beactive at the output of a stage at the same time once a specific numberof stages 610 have been passed through. In this case, a particularlylarge sum of lines occurs at the specific stage, said sum of lines beingcalculated by the respective summing means 630. An occurrence of such alarge sum of lines may result in that the comparator 634 of therespective stage outputs an active signal which in turn is forwarded,via the cascade of delay elements 636, to the output 640 of the Houghtransform means. Thus, a location of a course of curve in the imagesection which is input to the Hough transform means 600 in the form oftime signals 612, 614, 616 may be inferred from a temporal position ofan activity on the output signal at the output 640 of the Houghtransform means 600.

It shall also be noted that it is advantageous that a predefined signal(also referred to as a central signal) among the signals 612, 614, 616pass through the stages 610 of the Hough transform means 600 as fast asan output signal from the outputs of the comparators 634 which isforwarded by the chain of delay elements 636. In other words, at leastone of the input signals 612, 614, 616 propagates in parallel and at thesame speed as the output signals of the comparators 634. In this manner,one may achieve that the output signal which is present at the output640 of the Hough transform means 600 and which is based on the signalsof the comparators 634 which are forwarded in the cascade of delayelements 636, bears a direct statement on the point in time of theoccurrence of a bent line segment in the input signals 612, 614, 616. Inthis context, the point in time of the occurrence of an activity on theoutput signal at the output 640 of the Hough transform means 600provides a statement on the point in time when a bent course of line wasinput into the Hough transform means in the form of input signals 612,614, 616. The point in time of the presence of a bent course of samplein the signals 612, 614, 616 obviously allows direct conclusions to bedrawn as to a spatial locations of the bent course of curve in theraster image underlying the signals 612, 614, 616.

In addition, it shall be noted that with the configuration indicated,wherein at least one of the signals 612, 614, 616 propagates through thestages 610 as fast as the output signals of the comparators 634, theexact shape of the curvature, i.e. the curvature radius, for example, ina bent curve only has an influence as to which of the stages 610 acomparator 634 becomes active in. However, in the configuration shown,the precise shape of the bent course of curve has no influence on thepoint in time when an activity occurs at the output 640 of the Houghtransform means 600.

It may therefore be established that the Hough transform means 600 shownin FIG. 4 is suited to determine the location of a bent course of curvein a raster image in a very efficient manner in that the raster image(or a section thereof) is converted to a plurality of parallel signalswhich will then pass through several stages of the Hough transform means600 at different speeds. By forming a sum of columns at the outputs ofthe stages 610, one may detect when at least a predefined number ofsignals are active at the outputs of the stages at the same time, whichagain indicates that the original course of curve has been “unbent”.

Advantageously, the Hough transform means 600 is designed, by suitablyselecting delay elements 620 or bypasses 624, to unbend any courses ofcurve which are described by signals 612, 614, 616 and which mayapproximate the ellipse at the first ellipse point, the second ellipsepoint, the third ellipse point, or the fourth ellipse point. Also,advantageously, only such courses of curve which may approximate anellipse at the first ellipse point, the second ellipse point, the thirdellipse point, or the fourth ellipse point will be unbent. Thus, theHough transform means 600 of FIG. 4 is suited to identify the first bentline segment, the second bent line segment, the third bent line segment,or the fourth bent line segment. The point in time when an output signalis present at the output 640 of the Hough transform means 600 describesa location of the identified course of curve in the raster image onwhich the signals 612, 614, 616 are based, i.e. a parameter of the firstbent line segment, the second bent line segment, the third bent linesegment, or the fourth bent line segment.

FIG. 5 a shows a graphical representation of an approach of moving agraphic image through a pattern detection means. Specifically, FIG. 5 ashows moving an image or raster image through the Hough transform means600 shown in FIG. 4 (also referred to as a Hough array) on acolumn-by-column basis.

FIG. 5 a shows a raster image 710 consisting of a plurality of rasterlines 720 and a plurality of raster columns 730. What is also shown aregroups 740 of advantageously five raster columns 730 each, it beingassumed that five raster columns in each case being supplied, at thesame time, to the Hough transform means 600 in parallel in the form ofsignals 612, 614, 616. For further details, reference shall be made tothe graphical representation 300 of FIG. 1.

FIG. 5 b shows a graphical representation of time signals which areformed during a conversion of a raster image to parallel time signals.The graphical representation of FIG. 5 b is designated by 750 in itsentirety. The graphical representation 750 shows a raster image 760comprising a plurality of inactive raster points or image points 762 anda plurality of active raster points or image points 764 which are markedby hatching. The active raster points or image points 764 advantageouslydescribe a course of curve. As was already described above, the rasterimage 760 comprises a plurality of raster lines 770 and a plurality ofraster columns 772. It is also assumed that time signals are formed onthe basis of an image section 780 comprising a group of seven rastercolumns. For example, a first time signal 782 is associated with a firstraster column 784 contained within the group 780 of raster columns. Thetime signal 782 here is formed by scanning the raster image 760 alongthe associated raster column 784 on a line-by-line basis. Similarly, asecond time signal 786 is formed by scanning the second raster column788 among the group 780 of raster columns on a line-by-line basis.Observation of the time behaviors clearly shows that in the scanningdirection described, active raster points located in the same rasterline of the raster image 760 result in simultaneous activity pulses onthe time signals 782, 786, 790. A horizontal line, that is, a lineextending within a raster line, thus becomes noticeable in the timesignals 782, 786, 790 by simultaneous pulses on the time signals 782,786, 790.

If it is assumed that the time signals 782, 786, 790 are supplied to aHough transform means 600 as input signals 612, 614, 616, and that thesignals 612, 614, 616 are delayed to different degrees at individualstages 610 of the Hough transform means 600, it becomes clear that thedelay of the time signals 782, 786, 790, which is effected to varyingdegrees, corresponds to a distortion of the raster image 760, as aresult of which a bent course of curve may be bent into a straight line.However, a straight line which corresponds to simultaneous activity ofseveral of the time signals 782, 786, 790 may be detected in the Houghtransform means 600, as was described above.

Utilization of a Hough transformer for determining elements of writingenables, as was already mentioned above, realizing a particularlyreliable detection of writing system.

While this invention has been described in terms of several embodiments,there are alterations, permutations, and equivalents which fall withinthe scope of this invention. It should also be noted that there are manyalternative ways of implementing the methods and compositions of thepresent invention. It is therefore intended that the following appendedclaims be interpreted as including all such alterations, permutationsand equivalents as fall within the true spirit and scope of the presentinvention.

The invention claimed is:
 1. A device for detecting characters in animage, comprising: a Hough transformer arranged to identify, asidentified elements of writing, circular arcs or elliptical arcs in theimage or in a preprocessed version of the image; a character descriptiongenerator arranged to acquire, on the basis of the identified circulararcs or elliptical arcs, a character description which describeslocations of the identified circular arcs or elliptical arcs; and adatabase comparator arranged to compare the character description with aplurality of comparative character descriptions which have charactercodes associated with them, so as to provide, as a result of thecomparison, a character code of a detected character; wherein the Houghtransformer is arranged to identify, as identified elements of writing,individual circular arcs or elliptical arcs, which approximate a courseof line of a character within a surrounding of local extremes, in theimage or in a preprocessed version of the image, and to provideinformation about an orientation of an identified circular arc orelliptical arc; and the character description generator is arranged toacquire, based on the identified circular arcs or elliptical arcs andthe information about the orientation, a character description whichdescribes locations of the individual identified circular arcs orelliptical arcs.
 2. The device according to claim 1, wherein thecharacter description of an arc comprises information indicating whetherthe arc is curved upward, downward, to the left or to the right.
 3. Thedevice according to claim 1, wherein the Hough transformer is arrangedto provide, when detecting a circular arc or elliptical arc, informationabout an extreme point of the circular arc or elliptical arc; andwherein the character description describes a position of the extremepoint.
 4. The device according to claim 1, wherein an extreme point ofan arc is a point of the arc which is located farthest in a predefineddirection.
 5. The device according to claim 1, wherein the Houghtransformer is arranged to identify a plurality of straight linesections, which run through the image in different directions, asidentified elements of writing.
 6. The device according to claim 5,wherein the Hough transformer is arranged to provide information about alocation, a length or a direction of an identified straight linesection, and wherein the character description generator is arranged touse the information, provided by the Hough transformer, about theidentified straight line section for producing the characterdescription.
 7. The device according to claim 1, wherein the characterdescription generator is arranged to acquire, as the characterdescription, a description of a character which describes the characteras an ordered description of identified elements of writing.
 8. Thedevice according to claim 7, wherein the character description generatoris arranged to order the character description in such a manner that theordered arranged of identified elements of writing describe a continuousline of writing.
 9. The device according to claim 1, wherein the Houghtransformer is arranged to provide information about a location, an arclength, a radius of curvature or an angle of curvature of the identifiedcircular arcs or elliptical arcs, and wherein the character descriptiongenerator is arranged to utilize the information provided by the Houghtransformer about the location, the arc length, the radius of curvatureor the angle of curvature of the identified circular arcs or ellipticalarcs for generating the character description.
 10. The device accordingto claim 1, wherein the character description generator is arranged togenerate the character description in such a manner that the characterdescription comprises a description of relative locations of circulararcs or elliptical arcs belonging to a character.
 11. The deviceaccording to claim 1, wherein the device comprises a line-of-writingdetector arranged to identify, on the basis of locations of the elementsof writing identified by the Hough transformer, a line along which thedetected characters are arranged.
 12. The device according to claim 11,wherein the line-of-writing detector is arranged to determine, as a lineof writing, a lower line, a base line, a center line, or an upper lineof characters on the basis of locations of the elements of writingidentified by the Hough transformation.
 13. The device according toclaim 11, wherein the line-of-writing detector is arranged to determine,as a line of writing, a line on which more than a predefined number ofextremes of identified circular arcs or elliptical arcs are located. 14.The device according to claim 11, wherein the character descriptiongenerator is arranged to generate the character description in such amanner that the character description describes information aboutlocations of the identified elements of writing in relation to at leastone detected line of writing.
 15. The device according to claim 1,wherein the character description generator is arranged to receive, intothe character description for identified circular arcs or ellipticalarcs, information provided by the Hough transformer about an orientationof the circular arc or of the elliptical arc.
 16. The device accordingto claim 1, wherein the character description generator is arranged toreceive, into the character description for identified circular arcs orelliptical arcs, information provided by the Hough transformer about aposition of an extreme point of the identified circular arc or of theidentified elliptical arc.
 17. The device according to claim 1, whereinthe Hough transformer is arranged to identify only such circular arcs orelliptical arcs whose radii of curvature are smaller, in terms ofamount, than a predefined maximum admissible radius of curvature. 18.The device according to claim 1, wherein the character descriptiongenerator is arranged to generate a description of the character byjoining together selected adjacent identified character elements,wherein the character description generator is arranged to select theselected adjacent identified character elements employed for thedescription of the character from a totality of identified characterelements such that the selected adjacent identified character elementsdescribe a continuous course of line from a predefined starting point toa predefined end point.
 19. The device according to claim 1, wherein thecharacter description generator is arranged to generate, on the basis ofidentified character elements, a feature vector describing successiveportions of a character.
 20. The device according to claim 1, whereinthe database comparator is arranged to compare a feature vector, whichcomprises the information of the character description, to a pluralityof comparative feature vectors associated with comparative characters,so as to acquire a measure of differences between the feature vector andthe comparative vectors, and to determine, on the basis of the measureof the differences, a character code belonging to the feature vector.21. The device according to claim 1, wherein the device comprises anedge detector arranged to detect edges in the image and to generate, onthe basis of the image, an edge image as the preprocessed version of theimage.
 22. The device according to claim 1, wherein the device comprisesa letter separator arranged to provide or to mark, for detection of acharacter, a section of the image which comprises only one character.23. The device according to claim 1, wherein the device comprises aconnectivity number calculator arranged to calculate a Eulerconnectivity number on the basis of an image content of an image sectionof the image which comprises one character; and wherein the devicefurther comprises a connectivity number examiner arranged to compare theEuler connectivity number calculated for the image section to apredefined comparative connectivity number comprised in a database andassociated with a character detected in the image section, so as toacquire reliability information carrying information about a reliabilityof a detection of a character.
 24. A method of detecting characters inan image, comprising: Hough transforming the image or a preprocessedversion of the image so as to identify, as identified elements ofwriting, circular arcs or elliptical arcs in the image or in apreprocessed version of the image; producing a character description onthe basis of the identified circular arcs or elliptical arcs, thecharacter description describing locations of the identified circulararcs or elliptical arcs; and comparing the character description with aplurality of comparative character descriptions having character codesassociated with them, so as to provide, as a result of the comparison, acharacter code of a detected character; wherein the Hough transformingcomprises identifying, as identified elements of writing, individualcircular arcs or elliptical arcs, which approximate a course of line ofa character in a surrounding of local extremes, in the image or in apreprocessed version of the image; and the method further comprises:providing information about an orientation of an identified circular arcor elliptical arc; and producing a character description describinglocations of the individual identified circular arcs or elliptical arcson the basis of the identified circular arcs or elliptical arcs and ofthe information about the orientation.
 25. A non-transitory computerreadable medium having a computer program for performing, when thecomputer program runs on a computer, the method of detecting charactersin an image, the method comprising: Hough transforming the image or apreprocessed version of the image so as to identify, as identifiedelements of writing, circular arcs or elliptical arcs in the image or ina preprocessed version of the image; producing a character descriptionon the basis of the identified circular arcs or elliptical arcs, thecharacter description describing locations of the identified circulararcs or elliptical arcs; and comparing the character description with aplurality of comparative character descriptions having character codesassociated with them, so as to provide, as a result of the comparison, acharacter code of a detected character; wherein the Hough transformingcomprises identifying, as identified elements of writing, individualcircular arcs or elliptical arcs, which approximate a course of line ofa character in a surrounding of local extremes, in the image or in apreprocessed version of the image; and the method further comprises:providing information about an orientation of an identified circular arcor elliptical arc; and producing a character description describinglocations of the individual identified circular arcs or elliptical arcson the basis of the identified circular arcs or elliptical arcs and ofthe information about the orientation.
 26. A device for detectingcharacters in an image, comprising: a Hough transformer arranged toidentify, as identified elements of writing, circular arcs or ellipticalarcs in the image or in a preprocessed version of the image; a characterdescription generator arranged to acquire, on the basis of theidentified circular arcs or elliptical arcs, a character descriptionwhich describes locations of the identified circular arcs or ellipticalarcs; a database comparator arranged to compare the characterdescription with a plurality of comparative character descriptions whichhave character codes associated with them, so as to provide, as a resultof the comparison, a character code of a detected character; aconnectivity number calculator arranged to calculate a Eulerconnectivity number based on an image content of an image section of theimage which comprises one character; and a connectivity number examinerarranged to compare the Euler connectivity number calculated for theimage section to a predefined comparative connectivity number in adatabase and associated with a character detected in the image section,so as to acquire reliability information carrying information about areliability of a detection of a character.
 27. A method of detectingcharacters in an image, comprising: Hough transforming the image or apreprocessed version of the image so as to identify, as identifiedelements of writing, circular arcs or elliptical arcs in the image or ina preprocessed version of the image; producing a character descriptionbased on the identified circular arcs or elliptical arcs, the characterdescription describing locations of the identified circular arcs orelliptical arcs; comparing the character description with a plurality ofcomparative character descriptions having character codes associatedwith them, so as to provide, as a result of the comparison, a charactercode of a detected character; calculating a Euler connectivity numberbased on an image content of an image section of the image whichcomprises one character; and comparing the Euler connectivity numbercalculated for the image section to a predefined comparativeconnectivity number in a database and associated with a characterdetected in the image section, so as to acquire reliability informationcarrying information about a reliability of a detection of a character.28. A non-transitory computer readable medium having a computer programfor performing, when the computer program runs on a computer, a methodof detecting characters in an image, the method comprising: Houghtransforming the image or a preprocessed version of the image so as toidentify, as identified elements of writing, circular arcs or ellipticalarcs in the image or in a preprocessed version of the image; producing acharacter description based on the identified circular arcs orelliptical arcs, the character description describing locations of theidentified circular arcs or elliptical arcs; comparing the characterdescription with a plurality of comparative character descriptionshaving character codes associated with them, so as to provide, as aresult of the comparison, a character code of a detected character;calculating a Euler connectivity number based on an image content of animage section of the image which comprises one character; and comparingthe Euler connectivity number calculated for the image section to apredefined comparative connectivity number comprised in a database andassociated with a character detected in the image section, so as toacquire reliability information carrying information about a reliabilityof a detection of a character.